1 May 2003 Two methods for tracking small animals in SPECT imaging
Author Affiliations +
Proceedings Volume 5132, Sixth International Conference on Quality Control by Artificial Vision; (2003) https://doi.org/10.1117/12.514951
Event: Quality Control by Artificial Vision, 2003, Gatlinburg, TE, United States
High-resolution single photon emission computed tomography (SPECT) and X-ray computed tomography (CT) imaging have proven to be useful techniques for non-invasively monitoring mutations and disease progression in small animals. A need to perform in vivo studies of non-anesthetized animals has led to the development of a small-animal imaging system that integrates SPECT imaging equipment with a pose-tracking system. The pose of the animal is monitored and recorded during the SPECT scan using either laser-generated surfaces or infrared-reflective markers affixed to the animal. The reflective marker method measures motion by stereoscopically imaging an arrangement of illuminated markers. The laser-based method is proposed as a possible alternative to the reflector method with the advantage that it is a non-contact system. A three-step technique is described for calibrating the surface acquisition system so that quantitative surface measurements can be obtained. The acquired surfaces can then be registered to a reference surface using the iterative closest point (ICP) algorithm to determine the relative pose of the live animal and correct for any movement during the scan. High accuracy measurement results have been obtained from both methods.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ryan A. Kerekes, Ryan A. Kerekes, James S. Goddard, James S. Goddard, Shaun S. Gleason, Shaun S. Gleason, Michael J. Paulus, Michael J. Paulus, Andrew G. Weisenberger, Andrew G. Weisenberger, M. F. Smith, M. F. Smith, B. Welch, B. Welch, } "Two methods for tracking small animals in SPECT imaging", Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); doi: 10.1117/12.514951; https://doi.org/10.1117/12.514951

Back to Top